Ambient fine particulate matter (PM2.5) has severe adverse health impacts, making it crucial to reduce PM2.5 exposure for public health. Meteorological and emissions factors, which considerably affect the PM2.5 concentrations in the atmosphere, vary substantially under different climate change scenarios. In this work, global PM2.5 concentrations from 2021 to 2100 were generated by combining the deep learning technique, reanalysis data, emission data, and bias-corrected CMIP6 future climate scenario data. Based on the estimated PM2.5 concentrations, the future premature mortality burden was assessed using the Global Exposure Mortality Model. Our results reveal that SSP3-7.0 scenario is associated with the highest PM2.5 exposure, with a global concentration of 34.5 μg/m3 in 2100, while SSP1-2.6 scenario has the lowest exposure, with an estimated of 15.7 μg/m3 in 2100. PM2.5-related deaths for individuals under 75 years will decrease by 16.3 and 10.5% under SSP1-2.6 and SSP5-8.5, respectively, from 2030s to 2090s. However, premature mortality for elderly individuals (>75 years) will increase, causing the contrary trends of improved air quality and increased total PM2.5-related deaths in the four SSPs. Our results emphasize the need for stronger air pollution mitigation measures to offset the future burden posed by population age.
Keywords: PM2.5; climate change; deep learning; global; mortality.